Semantic parsers, such as DynSP, deal with the linking task using neural language models. These models map natural language expressions into vectors in a hidden semantic space where the vectors of the expressions that are semantically similar are close to each other. As Equation REF  shows, words in the question and the target column name are encoded into vectors using a pre-trained language model for matching. Thus, the performance of semantic parsers relies on the quality of the language model they use. The state-of-the-art neural language models (e.g., BERT [1]}, RoBERTa [2]}, and UniLM [3]}) are typically pre-trained on large amounts of text data and then fine-tuned to various downstream NLP tasks.
